'Continuous valued' diaporamas de présentation

Discrete-Time Signals: Time-Domain Representation

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Data Mining: Classification and Prediction

Data Mining: Classification and Prediction. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation.

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Data Mining: Classification and Prediction

Data Mining: Classification and Prediction. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation.

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Chapter 6. Classification and Prediction

What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation. Support Vector Machines (SVM) Associative classification

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Chapter 6. Classification and Prediction

What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation. Support Vector Machines (SVM) Associative classification

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Classification and Prediction

What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation. Support Vector Machines (SVM) Associative classification

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CENG 464 Introduction to Data Mining

CENG 464 Introduction to Data Mining. Supervised vs. Unsupervised Learning. Supervised learning (classification) Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations

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CENG 464 Introduction to Data Mining

CENG 464 Introduction to Data Mining. Supervised vs. Unsupervised Learning. Supervised learning (classification) Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations

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Data Mining: Classification

Data Mining: Classification. Classification and Prediction. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian Classification Classification by backpropagation

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Classification and Prediction

Classification and Prediction. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian Classification Classification by backpropagation Classification based on concepts from association rule

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? 4 ? ???????

??????? ? ?????? ? ??. ? 4 ? ???????. ???? ??? ???????????. ????. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian Classification Prediction Summary Reference. Classification vs. Prediction.

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Artificial Intelligence in Game Design

Artificial Intelligence in Game Design. Lecture 6: Fuzzy Logic and Fuzzy State Machines. Problems with Discrete States. Characters may be in multiple states simultaneously Particularly emotional states Hit ? angry Low HP ? frightened May indicate contradictory responses

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Classification

Classification. CS 685: Special Topics in Data Mining Jinze Liu. Classification and Prediction. What is classification? What is regression? Issues regarding classification and prediction Classification by decision tree induction Scalable decision tree induction.

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Physical Limits of Communication

Physical Limits of Communication. Madhu Sudan Microsoft Research. Joint work with Sanjeev Khanna ( U . Penn . ). TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A. Reliable Communication. Sender. Receiver. Encoder. Channel. Decoder.

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Classification

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LECTURE 13: REESTIMATION, EM AND MIXTURES

LECTURE 13: REESTIMATION, EM AND MIXTURES. Objectives: Reestimation Equations Continuous Distributions Gaussian Mixture Models EM Derivation of Reestimation

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